Methods exist for recognizing voices and images, for instance, but historically they have not been very robust to occlusion. Occlusion often occurs in the context of data objects generally, and appears with reference to faces when an image is captured of a human wearing a hat, sunglasses, or varying levels of facial hair coverage. Additional unexpected objects such as noise, reflections, etc., may also occlude a data object that needs to be recognized or identified. These occlusions may include noise or electromagnetic interference of voice data when attempting to recognize a voice. Many of the existing methods require prior knowledge of a test object or image and/or prior knowledge of the location of, or other information in regards to, the occlusion within the test object or image.
One method that has been used for image recognition is a classical recognition algorithm called nearest-neighbor (NN), which will be compared with the methods disclosed herein below. The NN classifier computes the Euclidean distances between a test vector y and training vectors v1, . . . , vn, and assigns the membership of y to be equal to one of the training vectors with the closest distance.
Another method that has been used for image recognition is a related algorithm called nearest subspace (NS), e.g., a minimum distance to the subspace spanned by images of each subject. As will be shown, the methods of the present disclosure are far superior in rates of recognition, especially when in the presence of occlusion.